# Super parameters
clamp = 2.0
channels_in = 3
log10_lr = -4.5
lr = 10 ** log10_lr
# 修改过
epochs = 1
weight_decay = 1e-5
init_scale = 0.01

# 各个损失函数的权重
lamda_reconstruction = 5
lamda_guide = 1
lamda_low_frequency = 1
# GPU编号
device_ids = [0]

# Train:
batch_size = 4 # 我改了
cropsize = 224
betas = (0.5, 0.999) # Adam优化器中的超参数
weight_step = 1000   # 每多少epoch学习率会衰减
gamma = 0.5 # 定义学习率的衰减因子

# Val:验证
cropsize_val = 1024
batchsize_val = 2
shuffle_val = False
val_freq = 50  #验证频率


# Dataset路径
TRAIN_PATH = 'Dataset/DIV2K/DIV2K_train_HR/'
VAL_PATH = 'Dataset/DIV2K/DIV2K_valid_HR/'
format_train = 'png'
format_val = 'png'

# Display and logging:
loss_display_cutoff = 2.0
loss_names = ['L', 'lr']
silent = False
live_visualization = False 
progress_bar = False


# Saving checkpoints:
MODEL_PATH = 'model/'
checkpoint_on_error = True
SAVE_freq = 50  # 保存频率

IMAGE_PATH = 'image/'
IMAGE_PATH_cover = IMAGE_PATH + 'cover/'
IMAGE_PATH_secret = IMAGE_PATH + 'secret/'
IMAGE_PATH_steg = IMAGE_PATH + 'steg/'
IMAGE_PATH_secret_rev = IMAGE_PATH + 'secret-rev/'

# Load:
suffix = 'model.pt'
tain_next = False
trained_epoch = 0
